| 02 Dec 2025
Assessing the Impacts of Land Use and Land Cover-Based Drought Adaptation Measures with an Eco-Hydrological Model
Abstract. Europe has warmed by about 1.5 °C above pre-industrial levels and endured record-breaking droughts from 2018–2020, underscoring the need for adaptation to water scarcity. This study examines the potential of targeted land use and land cover (LULC) changes to modify water fluxes and soil moisture storage for greater hydrologic drought resilience. Evaluated measures comprise replacing grain corn with sorghum on agricultural fields, converting coniferous forests (spruce, pine) to broadleaved stands (beech, oak), and mitigating imperviousness in built-up areas.
The study area, the 1,983 km² Upper Lippe catchment in Germany, and the exceptionally dry period of 2011–2020, are suitable conditions to address the research question for a temperate region. The assessment was conducted with the eco-hydrological model SWAT+ and novel approaches were implemented to accurately parameterize agricultural land use and management, dominant tree species, and the realistic impervious fraction of built-up areas within the model using publicly available existing data products and studies. After applying a calibration strategy that specifically targeted low-flow periods, the model performs well in the study period combining a good representation of low-flow periods with a standardized Root Mean Square Error for flows exceeding a 70 % probability threshold of 0.14 and while also maintaining robust overall streamflow dynamics with a modified Kling-Gupta Efficiency (KGE’) of 0.90 at the catchment outlet. The parameterization and calibration approaches can serve as references for model setups addressing similar ecoregions and research questions.
In the adapted agricultural areas, the evapotranspiration coefficient decreased by -11.7 percentage points (pp, area weighted median of the annual average) with reductions concentrated in the vegetation period leading to increases in soil moisture content. In response, the drainage flow coefficient increased by +3.3 pp with increases concentrated in the winter months and the groundwater recharge coefficient increased by +4.8 pp with a relatively uniform distribution throughout the year. The evapotranspiration coefficient from the adapted forested areas was reduced by -15.9 pp (from 67.5 %) with reductions occurring outside of the summer months. Here, increased soil moisture content increases the lateral flow coefficient by +9.0 pp and the groundwater recharge coefficient by 3.8 pp. Surface runoff increases only slightly, with enhanced surface runoff primarily occurring in mountainous areas where broadleaf trees provide less rainfall interception during winter dormancy. In the adapted built-up areas, reductions in impervious surfaces led to an increased groundwater recharge coefficient (+0.4 pp) and a decreased surface runoff coefficient (-3.6 pp), while the evapotranspiration coefficient increased (+2 pp), particularly in summer. Plant-available moisture in the topsoil increased in the adapted agricultural and forested areas across all modeled adaptation measures, reducing magnitude and duration of water stressed periods. These results demonstrate that LULC adaptations can shift landscape water balance by reducing evapotranspiration and increasing infiltration, thereby strengthening drought resilience and offering co‑benefits such as urban cooling. Such insights can guide policy and land management toward scalable, land use‑based solutions for extreme weather resilience under a warming global climate.
Competing interests: The authors declare that they have no conflict of interest. PW is a member of the editorial board of HESS.
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The authors investigated how different targeted LULC change strategies could enhace drought resilience due to improved soild moisture retention and reduced evaporation losses. The investigation spanned agricultural fields, desealing and forest changes in a German test catchment. Investigations focused on a dry period (2011 and 2020) and used the widely used SWAT+ model. While the study provides valuable insights and is well written, I have several limitations that should be addressed and that need major revisions. Please note that detailled comments are in the attached PDF. Generally the paper is already well written but could benefit from some methodological clarity here and there to make i t even easier for the reader.
My main concerns are:
1. The choice of the Hargreaves method. This choice in my opinion leads to a logical flaw in the paper that does not the reflect the further efforts put in by the authors. The choice of the Hargreaves methods is especially surprising as the catchment is in data-rich Germany and moreover a comprehensive meteorological dataset was used (HYRAS) that already contains all required information (except wind) to apply the Penman-Monteith method.
I personally do not see a single reason that speaks for this choice. This is indirectly confirmed by the authors as they have done a very uncommon calibration tweak which is the change of the empirical Hargreaves coefficient of 0.0023 to reduce evapotranspiration. In detail, the Harggreaves method drastically conpromises physical or more precisely plant physiological interpretability. This is in a sense completely contrasting the efforts the authors have chosen to implement small-scale crop and land use information, the comprehensive adaption of the crop/plant databse, cropping calendars and integration of novel availale datasets in the model setup. This was a missed opportunity to make the results stronger and increase the interpretability of the results. The authors should provide strong arguments about this choice, indeed using Penman in a second version would be the best solution, albeit I fear time constraints make this an impossible thing.
2. No impacts on the catchment-scale were provided. If I understood correctly all presented results refer to the land-use/ scenario specific areas (those that were changed in the scenarios. The authors even ackowledged this in the conclusion section, where they state other studies should do that. However, I appeal to the scientific ambitions of the authors that this should already have been done in their study and should be caught up on during the review. The information is already there and easy to implement. At least it must be clear in which order of magnitude the aggregated effects of the 3 scenarios are for the catchment.
3. The authors have chosen an unconventional calibration approach of using just 1 iteration and shooting with 19,200 simulations around thre. I see multiple constraints here (see PDF comments) and I would need more information how this choice is justified. Though, the 19,200 simulations seem a bit arbitrarya s well.
4. Another missed opportunity that contrasts the strong efforts the authors put into the model setup and especially their spatial representation of details and information, is why the additional gauges were not used for calibration and just an outlet-only approach is chosen. Although the cross validation results are decent I see some limitations here, as a better regionalization would have been possible (wrt. parameters). Please comment. I see no valid point why the 19,200 simulations could not have been performed also for the subbasins first. I guess model skill was squandered here.
5. The discussion sections significantly lacks a summary of potential limitations, actually the authors do barely comment on limitations at all and imply strong overstatements of the findings. This must be changed rigorously.
6. I am missing the initial links and further information of the changes the authors did in terms f the plant database. It seems significant changes were done here and these modifications could be highly beneficial for other users as well, yet, they are neither documented nor was sufficiently explained how these changes might influence the model results given the choice Hargreaves method. This gave huge potential for the discussion section but was ignored largely.
7. I was also missing the link to CN2, as Hargreaves was chosen I think many water balance changes could go back to CN changes rather than plant physiological reasons (how the authors state it), however, this impact on runoff generation/infiltration is not discussed or mentioned at all. CN2 impacts should be explored further. It needs to get clearer what the main drivers of the water balance in detail are, is it more the LAI change that scales PET of Hargreaves or is it the runoff generation change? What are the contributions, I think a lot can be done here.
8. Strongly connected to point 7, as the authors correctly discussed LAI impacts, I was wondering throughout the whole read why no LAI results were shown on the monthly/seasonal scale? This is a must in my opinion, especially given its later occurence in the discussion. Besides, to be less speculative, but more accurate and confident, I would recommend providing an ETA decomposition into canopy interception, transpiration and soil evaporation, to disentangle the changes in ETA for the scenarios.